There have been such known driving support systems as lane keeping assistance systems and adaptive cruise control (ACC) systems. In such driving support systems, the driving behavior of a driver must be supported appropriately; otherwise the support provided may become a burden on the driver. It is therefore important that the driving behavior of the driver be predicted accurately and that a support appropriate for the driving behavior of the driver be provided. Hence, driving behavior prediction apparatuses for predicting the driving behavior of a driver have been proposed.
In the driving behavior prediction apparatus described in the patent document 1, for example, the driving behavior of a driver is predicted by a statistical method using a hidden Markov model which is generally used for voice recognition. To be concrete, the driving behavior prediction apparatus is provided with plural models which correspond to driving behaviors (for example, turning right or left, and running straight) to be objects of recognition. Each of such models outputs an occurrence probability of the corresponding driving behavior by having driving data inputted, the driving data being represented by, for example, the depth to which the accelerator pedal is depressed, the depth to which the brake pedal is depressed, and the vehicle speed and acceleration. Thus, such models are generated based on driving data collected when driving behaviors to be objects of driving behavior prediction are practiced. The models calculate the occurrence probabilities of driving behaviors corresponding to them, respectively, by having actual driving data inputted, and the driving behavior corresponding to the model with the highest occurrence probability is predicted to be the driving behavior the driver will actually practice. In the driving behavior prediction apparatus using a hidden Markov model, to make the apparatus compatible with the driving behavior characteristics of various drivers, driving behavior patterns are recognized using models generated based on plural driving data. In the driving behavior prediction apparatus, unless the models are generated using driving data not much different from actual driving data, the result of predicting a driving behavior may differ from the driving behavior actually practiced by the driver, as a result, causing the driving behavior prediction accuracy to decline. Therefore, to accurately predict the driving behavior of each of plural drivers, it is necessary to generate and use models corresponding to individual drivers.
The driving behavior prediction apparatus described in the patent document 2 is provided with a storage device storing driving data corresponding to the driver of the own vehicle. In the driving behavior prediction apparatus, for model learning corresponding to the driver of the own vehicle, models required for a hidden Markov model are generated based on the driving data stored in the storage device.
(Patent Document 1) JP-A-H11-99849
(Patent Document 2) JP-A-2002-331850
In the driving behavior prediction apparatus configured as described in the patent document 2, however, model learning is conducted based only on the driving data showing driving behaviors of the driver of the own vehicle. Therefore, even in cases where a same driving behavior is repeated by the same driver, the driving data collected contains intra-individual differences which may result in cases where an actual driving behavior and a predicted driving behavior for the same person do not agree. This causes a problem that the results of model learning do not necessarily lead to improvement in the accuracy of driving behavior prediction.